New technologies combining digital media item players with dedicated software, together with new media distribution channels through computer networks (e.g., the Internet) are quickly changing the way people organize and play media items. As a direct consequence of such evolution in the media industry, users are faced with a huge volume of available choices that clearly overwhelm them when choosing what item to play in a certain moment.
This overwhelming effect is apparent in the music arena, where people are faced with the problem of selecting music from very large collections of songs. However, in the future, we might detect similar effects in other domains such as music videos, movies, news items, etc.
In general, our invention is applicable to any kind of media item that can be grouped by users to define mediasets. For example, in the music domain, these mediasets are called playlists. Users put songs together in playlists to overcome the problem of being overwhelmed when choosing a song from a large collection, or just to enjoy a set of songs in particular situations. For example, one might be interested in having a playlist for running, another for cooking, etc.
Different approaches can be adopted to help users choose the right options with personalized recommendations. One kind of approach is about using human expertise to classify the media items and then use these classifications to infer recommendations to users based on an input mediaset. For instance, if in the input mediaset the item x appears and x belongs to the same classification as y, then a system could recommend item y based on the fact that both items are classified in a similar cluster. However, this approach requires an incredibly huge amount of human work and expertise. Another approach is to analyze the data of the items (audio signal for songs, video signal for video, etc) and then try to match users preferences with the extracted analysis. This class of approaches is yet to be shown effective from a technical point of view.
Hosken (U.S. Pat. No. 6,438,579) describes a system and method for recommending media items responsive to query media items based on the explicit and implicit user characterizations of the content of the media items. Dunning, et. al. (U.S. Patent Application Pubs 2002/0082901 and 2003/0229537) disclose a system and method for discovering relationships between media items based on explicit and implicit user associations between said items. The present invention differs from Hosken and Dunning, et. al. in that it provides an automatic way to discover relationships between media items without requiring any user rating or any other knowledge from the user.
Lazarus et. al. (U.S. Pat. No. 6,134,532) describe a system and method for providing individually targeted advertising items to users based on observations of user behavior as quantified by representations for important content words in text-bearing materials accessed by a user. Advertising items responsive to the observed behavior of a user are identified by explicitly characterizing content words in the advertisement and by further characterizing candidate responsive items selected in this manner based on observations of user responses to them. Behrens et al. (U.S. Pat. No. 6,615,208) describe a method for associating textual materials similar to that of Lazarus et. al. for recommending items for purchase to a user based on the observed purchasing behavior of the user. The present invention differs from Lazarus et. al. and Behrens et. al. in that it is not based on user behavior other than how users in general associate items to form mediasets. Furthermore, Lazarus et al. and Behrens et. al. use standard methods for analyzing textual materials and the associated vector-space techniques as the basis for quantifying associations between textual representations of user behavior and textual content of advertising items while the present invention uses novel variants of associational methods for media items as the basis for associating media items.
Aggarwal and Yu (U.S. Pat. No. 6,487,539) describe a system for providing product recommendations to users based on extracting characterizations of products from textual descriptive materials about product features. Users are clustered with others users based on observed purchasing behaviors and the extracted descriptions of products purchased by a cluster of users are matched to extracted descriptions of other products and most similar products recommended to a user that is associated with that cluster of users. The present invention differs from Aggarwal and Yu in that the associations among media items are not based on characterizations of the items themselves, but rather on how they are grouped together.
Robinson (U.S. Pat. No. 5,918,014) describes a system and a process for presenting users of the World Wide Web with targeted advertising. Users are clustered based on observed web browsing of sites and purchasing activities while advertisements of probable interest to the users in a cluster are identified by presenting ads to users identified with the cluster and observing the interest of users in the advertisement as expressed by exploration of the ad and purchasing activity. Ads are selected for presentation to a specific user by first associating the user with an identified community, and then selecting from the ads associated with that community those, which conform more closely to the specific characteristics of the user, utilized to associate the user with the community. The present invention differs from Robinson in that media items are not identified for potential presentation based on the acceptance of the media item in response to random presentation to a group of users representative of the user, media items for presentation are instead affirmatively identified by the media items they contain.